A comparison with experiments that measured polarized reflectance of SiC nano pillars provides a validation case. The wavenumber of the dominant mode and two more do match, but differences remain in other minor modes. Results in this paper were produced with strict reproducibility practices, and we share reproducibility packages for all, including input files, execution scripts, secondary data, post-processing code and plotting scripts, and the figures (deposited in Zenodo). In view of the many challenges faced, we propose that reproducible practices make replication and validation more feasible. This article is part of the theme issue 'Reliability and reproducibility in computational science implementing verification, validation and uncertainty quantification in silico'.We carry out efforts to reproduce computational results for seven published articles and identify barriers to computational reproducibility. We then derive three principles to guide the practice and dissemination of reproducible computational research (i) Provide transparency regarding how computational results are produced; (ii) When writing and releasing research software, aim for ease of (re-)executability; (iii) Make any code upon which the results rely as deterministic as possible. We then exemplify these three principles with 12 specific guidelines for their implementation in practice. We illustrate the three principles of reproducible research with a series of vignettes from our experimental reproducibility work. We define a novel Reproduction Package, a formalism that specifies a structured way to share computational research artifacts that implements the guidelines generated from our reproduction efforts to allow others to build, reproduce and extend computational science. We make our reproduction efforts in this paper publicly available as exemplar Reproduction Packages. This article is part of the theme issue 'Reliability and reproducibility in computational science implementing verification, validation and uncertainty quantification in silico'.In this study, we investigate uncertainties in a large eddy simulation of the atmosphere, employing modern uncertainty quantification methods that have hardly been used yet in this context. When analysing the uncertainty of model results, one can distinguish between uncertainty related to physical parameters whose values are not exactly known, and uncertainty related to modelling choices such as the selection of numerical discretization methods, of the spatial domain size and resolution, and the use of different model formulations. While the former kind is commonly studied e.g. https://www.selleckchem.com/products/skf96365.html with forward uncertainty propagation, we explore the use of such techniques to also assess the latter kind. From a climate modelling perspective, uncertainties in the convective response and cloud formation are of particular interest, since these affect the cloud-climate feedback, one of the dominant sources of uncertainty in current climate models. Therefore we analyse the DALES model in the RICO case, a well-studied convection benchmark. We use the VECMA toolkit for uncertainty propagation, assessing uncertainties stemming from physical parameters as well as from modelling choices. We find substantial uncertainties due to small random initial state perturbations, and that the choice of advection scheme is the most influential of the modelling choices we assessed. This article is part of the theme issue 'Reliability and reproducibility in computational science implementing verification, validation and uncertainty quantification in silico'.Harnessing energy produced by thermonuclear fusion reactions has the potential to provide a clean and inexpensive source of energy to Earth. However, throughout the past seven decades, physicists learned that creating our very own fusion energy source is very difficult to achieve. We constructed a component-based, multiscale fusion workflow to model fusion plasma inside the core of a tokamak device. To ensure the simulation results agree with experimental values, the model needs to undergo the process of verification, validation and uncertainty quantification (VVUQ). This paper will go over the VVUQ work carried out in the multiscale fusion workflow (MFW), with the help of the EasyVVUQ software library developed by the VECMA project. In particular, similarity of distributions from simulation and experiment is explored as a validation metric. Such initial validation measures provide insights into the accuracy of the simulation results. This article is part of the theme issue 'Reliability and reproducibility in computational science implementing verification, validation and uncertainty quantification in silico'.We present a tutorial demonstration using a surrogate-model based uncertainty quantification (UQ) approach to study dynamic earthquake rupture on a rough fault surface. The UQ approach performs model calibration where we choose simulation points, fit and validate an approximate surrogate model or emulator, and then examine the input space to see which inputs can be ruled out from the data. Our approach relies on the mogp_emulator package to perform model calibration, and the FabSim3 component from the VECMA toolkit to streamline the workflow, enabling users to manage the workflow using the command line to curate reproducible simulations on local and remote resources. The tools in this tutorial provide an example template that allows domain researchers that are not necessarily experts in the underlying methods to apply them to complex problems. We illustrate the use of the package by applying the methods to dynamic earthquake rupture, which solves the elastic wave equation for the size of an earthquake and the resulting ground shaking based on the stress tensor in the Earth. We show through the tutorial results that the method is able to rule out large portions of the input parameter space, which could lead to new ways to constrain the stress tensor in the Earth based on earthquake observations. This article is part of the theme issue 'Reliability and reproducibility in computational science implementing verification, validation and uncertainty quantification in silico'.